Hi @Databricks2206,
For us we have try a couple of approaches
Option 1: DQX (Databricks Labs Data Quality Framework) - this is purpose-built for what you're describing. It ships a pre-built dashboard with three tabs, including a "Data Quality Summary" tab that gives aggregated quality statistics across all monitored tables, giving you a high-level view of your data quality status - plus a time-series tab and a full-snapshot tab per table. It deploys automatically when you install DQX via the Databricks CLI, or you can import the dashboard directly from the GitHub repo. See: https://databrickslabs.github.io/dqx/docs/guide/quality_dashboard/
Option 2: Native Lakehouse Monitoring - if you'd rather not add a labs/third-party framework, Databricks' built-in monitoring creates two Delta tables per monitored table: {output_schema}.{table_name}_profile_metrics and {output_schema}.{table_name}_drift_metrics (docs: https://docs.databricks.com/aws/en/lakehouse-monitoring/monitor-output). If you point every table's monitor at the same output_schema_name, all your tables' metrics land in one schema, namespaced by table name - so you can build a single UNION ALL view across them and put a regular AI/BI Dashboard on top. More manual than DQX's dashboard, but zero extra dependencies.
For "many tables, one view" specifically, I'd start with DQX's dashboard since that's exactly the gap it's built to fill - worth checking if the Data Quality Summary tab already covers your metrics before building something custom on Lakehouse Monitoring.
We are now basically forking the DQX, and customizing it ourself.
